Pilot, Process, and Predict
We start off with a 3-month pilot where we analyze and identify your key pain points. This gives us a clearer idea of what to target and which data sources to process. Combining a variety of variables, our advanced machine learning algorithms learn to aggregate data silos and discovers deep insights unseen by the human eye.
Our predictions are summarized and delivered into recommendable actions. For complete convenience, our AI-driven system sends this as a dashboard notification, and/or emails it directly to your inbox. Make intelligent, accurate predictions for better business decisions.
The Sources Behind Our Solution
Our machine learning models combine, analyze, and learn from a multitude of sources as seen below. Improving from each data point, we efficiently predict specific product demand, effectively creating stronger retail strategies for your store.
Inspect social media to see what you styles, colors, and patterns are trending
Identify patterns in local weather to see how it'll affect sales
Identify local holidays and promotions that may influence consumer decisions
CPI & Unemployment Rates
Examine how socio-economical changes may affect how and when people buy
Identify specific attributes of each product to see what sells and doesn't
Historical Sales Transactions
Analyze patterns of consumer's past transactions to predict future demand
Understanding How It Works
Collect & Analyze
Starting out the pilot, we collect 2-3 years of historical data from a multitude of sources, both internal and external. Our proprietary self-learning AI improves on each new data point it gets on its own, analyzing from data related to the brand, product, sales, macro, and online.
Often times, real-world data is disorganized or incomplete. To resolve such an issue, we perform a data mining technique called data pre-processing, which involves ‘cleaning’ the raw data into an understandable format.
We do this together with the process of ETL (extract, transform, load), where we select the data from sources, transform it accordingly, and import it into the specified system. The process of data cleaning and extraction can be seen as being part of the integration process.
Align Business Strategy
We sit down with you and figure out what sort of supply chain constraints you are experiencing, and the overall business objectives, as well as key decision dates required to achieving the optimal success of our service.
Train & Tune the Model
Once we have cleaned data to work with, we begin a multi-model approach in trying to find the best fit result that is suitable for your business needs.
In machine learning, there are different models that are befitting specific business problems. With so many variables to consider in retail, specifically fashion, we test and validate the results with hyper-parameters and adjust all the variables accordingly to get the most accurate predictions.
Visualize to Summarize
Once everything is fine-tuned and modeled, the data is then beautifully visualized onto your dashboard. Moreover, you can opt to have a summary of the insights sent to your mailbox, giving you greater convenience to the information.
Make Key Decisions
Our system provides AI-driven recommended actions based on the predictions made of consumer demand. At this point, it is time to make key business decisions that will help maximize profit and minimize inventory risks, as well as reduce markdowns.